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Woody vegetation cover - Landsat, JRSRP, Australian coverage, 2000-2010 

Ver: 1.0
Status of Data: completed
Update Frequency: notPlanned
Security Classification: unclassifiedunclassified
Record Last Modified: 2026-03-17
Viewed 3032 times
Accessed 502 times
Dataset Created: 2012-03-21
Dataset Published: 2021-10-07
Data can be accessed from the following links:
HTTPPoint-of-truth metadata URLHTTPCloud Optimised GeoTIFFsWMSlandscapes:woody_veg_cover_landsat_2000_2010HTTPLandscape Data VisualiserHTTPro-crate-metadata.json
How to cite this collection:
Gill, T. (2021). Woody vegetation cover - Landsat, JRSRP, Australian coverage, 2000-2010. Version 1.0. Terrestrial Ecosystem Research Network. Dataset. https://portal.tern.org.au/metadata/e4de7f56-f1a5-418e-9118-3220f6f365f8 
Three maps are available: 1) foliage projective cover, 2) forest extent, attributed with the foliage projective cover and 3) accuracy of the extent maps, which also acts as masks of forest and other wooded lands. Each pixel in map 1 estimates the fraction of the ground covered by green foliage. Each pixel in map 2 shows two pieces of information. The first is a classification of whether the vegetation is forest or not. The pixels classified as forest are attributed with the second piece of information: the foliage projective cover. Each pixel in map 3 is a class that provides information on the classification accuracies of the woody extent. These maps are derived from Landsat. 
Credit
We at TERN acknowledge the Traditional Owners and Custodians throughout Australia, New Zealand and all nations. We honour their profound connections to land, water, biodiversity and culture and pay our respects to their Elders past, present and emerging. This dataset was produced using data sourced from the US Geological Survey. 
Purpose
We realised that there was no easily accessible map of woody-vegetation cover of Australia, produced consistently across the continent, for land managers and ecologists to use at a local-scale. Researchers and governments have opened access to their field, airborne and satellite image data, making the task of creating such a map possible. We built on these efforts to create a map of woody-vegetation cover of Australia for the decade from 2000 to 2010. 
Lineage
Data not provided. 
Method DocumentationData not provided.
Procedure StepsData not provided.
Spatial Description
Australia 
Temporal Coverage
From null to on going 
Spatial Resolution

Data not provided.

Vertical Extent

Data not provided.

Data Quality Assessment Scope
The input imagery was processed to level L1T by the USGS. Geodetic accuracy of the product depends on the image quality and the accuracy, number, and distribution of the ground control points. 
Data Quality Report
Data not provided. 
Data Quality Assessment Outcome
The overall classification accuracy of the woody vegetation extent is 81.9%. The user's and producer's accuracy for the woody class were 85.6% and 90.6%, respectively. The user's and producer's accuracies for areas mapped as forest were high at 92.2% and 95.9% respectively. The user's and producer's accuracies for other wooded lands is 75.7% and 61.3%, respectively. Validation of woody foliage projective cover with field-measurements gave a coefficient of determination, R2 of 0.918 and a RMSE of 0.70. 
anzsrc-forlocal
Climate change impacts and adaptation
Environmental management
gcmdlocal
LAND USE/LAND COVER
VEGETATION COVER
Horizontal Resolution
30 meters - < 100 meters
Instruments
ETM+
TM
Parameters
foliage projective cover
persistent green vegetation fraction
Platforms
LANDSAT-5
LANDSAT-7
project
TERN Landscapes
Temporal Resolution
Weekly - < Monthly
Topic
environment
imageryBaseMapsEarthCover
Author
Gill, Tony
Gill, Tony
Contact Point
Gill, Tony
Data Enquiries, Earth Observation and Social Sciences (EOSS)
Publisher
Terrestrial Ecosystem Research Network
Gill, T., et al, 2017. A method for mapping Australian woody vegetation cover by linking continental-scale field data and long-term Landsat time series, International Journal of Remote Sensing, 38(3), pp 679-705.
Guerschman, JP., et al, 2015. Assessing the effects of site heterogeneity and soil properties when unmixing photosynthetic vegetation, non-photosynthetic vegetation and bare soil fractions from Landsat and MODIS data. Remote Sensing of Environment.
Flood, N., Danaher, T., Gill, T., Gillingham, S. 2013. An Operational Scheme for Deriving Standardised Surface Reflectance from Landsat TM/ETM+ and SPOT HRG Imagery for Eastern Australia. Remote Sensing
Zhu, Z. and Woodcock, C.E., 2012. Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment, 118, 83-94
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It is not recommended that these data sets be used at scales more detailed than 1:100,000. 
TERN services are provided on an “as-is” and “as available” basis. Users use any TERN services at their discretion and risk. They will be solely responsible for any damage or loss whatsoever that results from such use including use of any data obtained through TERN and any analysis performed using the TERN infrastructure.
Web links to and from external, third party websites should not be construed as implying any relationships with and/or endorsement of the external site or its content by TERN.

Please advise any work or publications that use this data via the online form at https://www.tern.org.au/research-publications/#reporting 
TERN services are provided on an “as-is” and “as available” basis. Users use any TERN services at their discretion and risk. They will be solely responsible for any damage or loss whatsoever that results from such use including use of any data obtained through TERN and any analysis performed using the TERN infrastructure.
Web links to and from external, third party websites should not be construed as implying any relationships with and/or endorsement of the external site or its content by TERN.

Please advise any work or publications that use this data via the online form at https://www.tern.org.au/research-publications/#reporting 
It is not recommended that these data sets be used at scales more detailed than 1:100,000.